Drones have always been a high-flying success at the annual CES show. The latest drone with a buzz: Sony's Airpeak drone, which promises to be an eye-in-the-sky for filmmakers. Sony did not offer a lot of information about the drone, but showed a video of it – outfitted with a Sony Alpha 7S III camera – tracking the electronics company's in-development Vision-S electric high-tech vehicle from above. Captured was stunning footage of the snowy, wooded mountainous Austrian landscape. The Airpeak, Sony says, is the smallest class of drones that can carry such a camera.
The Pentagon's Joint Artificial Intelligence Center has awarded a $93.3 million contract to General Atomics Aeronautical Systems Inc (GA-ASI), makers of the MQ-9 Reaper, to equip the drone with new AI technology. The aim is for the Reaper to be able to carry out autonomous flight, decide where to direct its battery of sensors, and to recognize objects on the ground. The contract, announced at the end of last month, builds on a successful test earlier this year. In some ways this is not a major development, more of an incremental step using existing technology. What makes it significant is the drone that is being equipped, and what it will be able to do afterwards.
General Atomics Aeronautical Systems, Inc. (GA-ASI) has demonstrated the DARPA-developed Collaborative Operations in Denied Environment (CODE) autonomy engine on the company's Avenger Unmanned Aircraft System (UAS). CODE was used in order to gain further understanding of cognitive Artificial Intelligence (AI) processing on larger UAS platforms for air-to-air targeting. Using a network-enabled Tactical Targeting Network Technology (TTNT) radio for mesh network mission communications, GA-ASI was able to demonstrate integration of emerging Advanced Tactical Data Links (ATDL), as well as separation between flight and mission critical systems. During the autonomous flight, CODE software controlled the manoeuvring of the Avenger UAS for over two hours without human pilot input. GA-ASI extended the base software behavioural functions for a coordinated air-to-air search with up to six aircraft, using five virtual aircraft for the purposes of the demonstration.
Humans might not have much involvement in mid-air refueling before long. Boeing has flown a test version of its MQ-25 tanker drone with a refueling pod attached for the first time, taking it one step closer to topping up military aircraft. The 2.5-hour flight showed that the autonomous drone's aerodynamics were sound with the wing-mounted pod it's expected to carry much of the time. The test drone, T1, is a precursor to an "engineering development" model that will take Boeing one step closer to a finished vehicle. This could be a crucial machine.
We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned Deep Neural Networks (DNNs) with strong Lipschitz properties. We evolve two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: i) spectral normalization for stability and generalization guarantees of unseen data and ii) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multi-robot motion planning and the nonlinear tracking control designs because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neural-Swarm2 is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors.
The vehicle Elon Musk sees as the key to fast travel around the Earth and multiplanetary living has only taken short hops so far, but its next trip will reach 50,000 feet. The plan is to test out its aerodynamic capabilities and attempt a landing flip maneuver. SpaceX's stream begins at 7 AM, but stay tuned for more information on exactly when the test will go down if you want to watch live -- this could be historic. After multiple delays, CD Projekt Red's highly anticipated RPG (based on the table-top game of the same name) arrives on PC and consoles, and Jessica Conditt has spent about 20 hours in the world on Night City. The game is too deep for that to give a comprehensive view of what it contains (she took six hours to get beyond the prologue and meet Keanu Reeves) but more than enough to see if its 80s-tinged vision of the future holds up.
A private rocket-launch startup unveiled its fully autonomous drone designed to drop a rocket in midair that shoots small satellites into orbit without a launchpad. Alabama-based company Aevum rolled out its Ravn X Autonomous Launch Vehicle at the Cecil SpacePort launch facility in Jacksonville, Fla., on Thursday. America is changing faster than ever! Add Changing America to your Facebook or Twitter feed to stay on top of the news. The 80-foot aircraft has a wingspan of 60 feet, stands 18 feet tall and is the world's largest Unmanned Aircraft System (UAS) by mass, weighing 55,000 pounds.
This paper proposes a multi-sensor based approach to detect, track, and localize a quadcopter unmanned aerial vehicle (UAV). Specifically, a pipeline is developed to process monocular RGB and thermal video (captured from a fixed platform) to detect and track the UAV in our FoV. Subsequently, a 2D planar lidar is used to allow conversion of pixel data to actual distance measurements, and thereby enable localization of the UAV in global coordinates. The monocular data is processed through a deep learning-based object detection method that computes an initial bounding box for the UAV. The thermal data is processed through a thresholding and Kalman filter approach to detect and track the bounding box. Training and testing data are prepared by combining a set of original experiments conducted in a motion capture environment and publicly available UAV image data. The new pipeline compares favorably to existing methods and demonstrates promising tracking and localization capacity of sample experiments.
However, in this more recent test, General Atomics did develop additional algorithms for CODE to support "behavioral functions for a coordinated air-to-air search." During the demonstration, a human operator then instructed the Avenger and its five virtual wingmen to carry out the aerial search mission, which they then performed autonomously. The CODE "engine" flew the physical Avenger drone for more than two hours, according to the company's press release. It's interesting to note that the instructions from the human operator were sent to the drone using a Tactical Targeting Network Technology (TTNT) radio via the well-established Link16 waveform. The Navy developed TTNT first for the EA-18G Growler and it is now a key component of the service's Block III upgrade package for its F/A-18E/F Super Hornets.
The world's first satellite launching drone, developed by a US-based space startup, will will be able to carry a new payload into orbit every 180 minutes, the firm claims. Aevum says the massive 80ft long drone, named the Ravn X, is fully autonomous, 70 per cent reusable, and can take off and land on runways as short as a mile long. Working in partnership with the US Space Force, the firm says it is'completely reimagining access to space' by focusing on autonomy and better logistics. The drone can take off from any runway to reach high altitude where it deploys a second stage that takes a small payload the rest of the way to space. After it has launched the second stage rocket into low Earth orbit, the drone flies itself back to its home runway, lands and then parks up in its hanger.